What Is Data Smoothing?

Data smoothing is done by using an algorithm to remove noise from a data set. This allows important patterns to more clearly stand out.

Data smoothing can be used to help predict trends, such as those found in securities prices, as well in economic analysis that can take into account the effects of seasonality or which can ignore one-time outliers.

Key Takeaways

  • Data smoothing uses an algorithm to remove noise from a data set, allowing important patterns to stand out, and can be used to predict trends such as those found in securities prices.
  • Different data smoothing models include the random method the use of moving averages.
  • While data smoothing can help predict certain trends, it will inherently lead to less information in the sample that may lead to certain data points being ignored.

Data Smoothing Explained

When data is compiled, it can be manipulated to remove or reduce any volatility, or any other type of noise. This is called data smoothing.

The idea behind data smoothing is that it can identify simplified changes in order to help predict different trends and patterns. It acts as an aid for statisticians or traders who need to look at a lot of data—that can often be complicated to digest—to find patterns they would not otherwise see.

To explain with a visual representation, imagine a one-year chart for Company X's stock. Each individual high point on the chart for the stock can be reduced while raising all the lower points. This would make a smoother curve, thus helping an investor make predictions about how the stock may perform in the future.

Smoothed data is generally preferred by economists because it better identifies changes in trend compared to unsmoothed data, which may appear more erratic and create false signals.

Data Smoothing Methods

There are different methods in which data smoothing can be done. Some of these include the randomization method, using a random walk, calculating a moving average, or conducting one of several exponential smoothing techniques.

A simple moving average (SMA) places equal weight on both recent prices and historical ones, while an exponential moving average (EMA) puts more weight on recent price data.

The random walk model is commonly used to describe the behavior of financial instruments such as stocks. Some investors believe that there is no relationship between past movement in a security's price and its future movement. Random walk smoothing assumes that future data points will equal the last available data point plus a random variable. Technical and fundamental analysts disagree with this idea; they believe future movements can be extrapolated by examining past trends.

Often used in technical analysis, the moving average smooths out price action while it filters out volatility from random price movements. This process is based on past prices, making it a trend-following—or lagging—indicator. As can be seen in the price chart below, the moving average (EMA) has the general shape and trend of the underlying daily price data, depicted by the candlesticks. The more days incorporated into the moving average, the more smoothed the line becomes.

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Image by Sabrina Jiang © Investopedia 2020 

Pros and Cons of Data Smoothing

Data smoothing can be used to help identify trends in the economy, securities such as stocks, consumer sentiment, or for other business purposes.

For example, an economist can smooth out data to make seasonal adjustments for certain indicators like retail sales by reducing the variations that may occur each month like holidays or gas prices.

There are downfalls to using this tool, however. Data smoothing doesn't always provide an explanation of the trends or patterns it helps identify. It also may lead to certain data points being ignored by emphasizing others.

Pros
  • Helps identify real trends by eliminating noise from the data

  • Allows for seasonal adjustments of economic data

  • Easily achieved through several techniques including moving averages

Cons
  • Removing data always comes with less information to analyze, increasing the risk of errors in analysis

  • Smoothing may emphasize analysts' biases and ignore outliers that may be meaningful

Example of Data Smoothing in Financial Accounting

An often-cited example of data smoothing in business accounting is to make an  allowance for doubtful accounts to change bad debt expense from one reporting period to another. For example, a company expects not to receive payment for certain goods over two accounting periods; $1,000 in the first reporting period and $5,000 in the second reporting period.

If the first reporting period is expected to have a high income, the company may include the total amount of $6,000 as the allowance for doubtful accounts in that reporting period. This would increase the bad debt expense on the income statement by $6,000 and reduce net income by $6,000. This would thereby smooth out a high-income period by reducing income. It's important for companies to use judgment and legal accounting methods when adjusting any accounts.